Lab 6
Chris Colvin
9/28/2020
Building Maps
library(tidyverse)## -- Attaching packages -------------------------------------------------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.0 v purrr 0.3.3
## v tibble 3.0.0 v dplyr 0.8.5
## v tidyr 1.1.2 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.5.0## -- Conflicts ----------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(maps)##
## Attaching package: 'maps'## The following object is masked from 'package:purrr':
##
## maplibrary(mapdata)
library(lubridate)##
## Attaching package: 'lubridate'## The following objects are masked from 'package:dplyr':
##
## intersect, setdiff, union## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionlibrary(viridis)## Loading required package: viridisLitelibrary(wesanderson)daily_report <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/04-02-2020.csv")) %>%
rename(Long = "Long_") ## Parsed with column specification:
## cols(
## FIPS = col_double(),
## Admin2 = col_character(),
## Province_State = col_character(),
## Country_Region = col_character(),
## Last_Update = col_character(),
## Lat = col_double(),
## Long_ = col_double(),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Active = col_double(),
## Combined_Key = col_character()
## )ggplot(daily_report, aes(x = Long, y = Lat, size = Confirmed/1000)) +
borders("world", colour = NA, fill = "grey90") +
theme_bw() +
geom_point(shape = 21, color='purple', fill='purple', alpha = 0.5) +
labs(title = 'World COVID-19 Confirmed cases',x = '', y = '',
size="Cases (x1000))") +
theme(legend.position = "right") +
coord_fixed(ratio=1.5)## Warning: Removed 54 rows containing missing values (geom_point).
# US Confirmed Cases
daily_report <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/04-05-2020.csv")) %>%
rename(Long = "Long_") %>%
filter(Country_Region == "US") %>%
filter (!Province_State %in% c("Alaska","Hawaii", "American Samoa",
"Puerto Rico","Northern Mariana Islands",
"Virgin Islands", "Recovered", "Guam", "Grand Princess",
"District of Columbia", "Diamond Princess")) %>%
filter(Lat > 0)## Parsed with column specification:
## cols(
## FIPS = col_character(),
## Admin2 = col_character(),
## Province_State = col_character(),
## Country_Region = col_character(),
## Last_Update = col_datetime(format = ""),
## Lat = col_double(),
## Long_ = col_double(),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Active = col_double(),
## Combined_Key = col_character()
## )ggplot(daily_report, aes(x = Long, y = Lat, size = Confirmed/1000)) +
borders("state", colour = "black", fill = "grey90") +
theme_bw() +
geom_point(shape = 21, color='purple', fill='purple', alpha = 0.5) +
labs(title = 'COVID-19 Confirmed Cases in the US', x = '', y = '',
size="Cases (x1000))") +
theme(legend.position = "right") +
coord_fixed(ratio=1.5)
Anisa Dhana’s Map
mybreaks <- c(1, 100, 1000, 10000, 10000)
ggplot(daily_report, aes(x = Long, y = Lat, size = Confirmed)) +
borders("state", colour = "white", fill = "grey90") +
geom_point(aes(x=Long, y=Lat, size=Confirmed, color=Confirmed),stroke=F, alpha=0.7) +
scale_size_continuous(name="Cases", trans="log", range=c(1,7),
breaks=mybreaks, labels = c("1-99",
"100-999", "1,000-9,999", "10,000-99,999", "50,000+")) +
scale_color_viridis_c(option="viridis",name="Cases",
trans="log", breaks=mybreaks, labels = c("1-99",
"100-999", "1,000-9,999", "10,000-99,999", "50,000+")) +
# Cleaning up the graph
theme_void() +
guides( colour = guide_legend()) +
labs(title = "Anisa Dhana's lagout for COVID-19 Confirmed Cases in the US'") +
theme(
legend.position = "bottom",
text = element_text(color = "#22211d"),
plot.background = element_rect(fill = "#ffffff", color = NA),
panel.background = element_rect(fill = "#ffffff", color = NA),
legend.background = element_rect(fill = "#ffffff", color = NA)
) +
coord_fixed(ratio=1.5)## Warning: Transformation introduced infinite values in discrete y-axis
## Warning: Transformation introduced infinite values in discrete y-axis## Warning in sqrt(x): NaNs produced## Warning: Removed 40 rows containing missing values (geom_point).
Mapping data to shapes
daily_report <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/04-02-2020.csv")) %>%
rename(Long = "Long_") %>%
filter(Country_Region == "US") %>%
group_by(Province_State) %>%
summarize(Confirmed = sum(Confirmed)) %>%
mutate(Province_State = tolower(Province_State))## Parsed with column specification:
## cols(
## FIPS = col_double(),
## Admin2 = col_character(),
## Province_State = col_character(),
## Country_Region = col_character(),
## Last_Update = col_character(),
## Lat = col_double(),
## Long_ = col_double(),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Active = col_double(),
## Combined_Key = col_character()
## )# load the US map data
us <- map_data("state")
# We need to join the us map data with our daily report to make one data frame/tibble
state_join <- left_join(us, daily_report, by = c("region" = "Province_State"))
# plot state map# plot state map
ggplot(data = us, mapping = aes(x = long, y = lat, group = group)) +
coord_fixed(1.3) +
# Add data layer
geom_polygon(data = state_join, aes(fill = Confirmed), color = "black") +
scale_fill_gradientn(colours =
wes_palette("Zissou1", 100, type = "continuous"),
trans = "log10") +
labs(title = "COVID-19 Confirmed Cases in the US'")
library(RColorBrewer)
# To display only colorblind-friendly brewer palettes, specify the option colorblindFriendly = TRUE as follow:
# display.brewer.all(colorblindFriendly = TRUE)
# Get and format the covid report data
report_03_27_2020 <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/04-02-2020.csv")) %>%
rename(Long = "Long_") %>%
unite(Key, Admin2, Province_State, sep = ".") %>%
group_by(Key) %>%
summarize(Confirmed = sum(Confirmed)) %>%
mutate(Key = tolower(Key))## Parsed with column specification:
## cols(
## FIPS = col_double(),
## Admin2 = col_character(),
## Province_State = col_character(),
## Country_Region = col_character(),
## Last_Update = col_character(),
## Lat = col_double(),
## Long_ = col_double(),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Active = col_double(),
## Combined_Key = col_character()
## )# dim(report_03_27_2020)
# get and format the map data
us <- map_data("state")
counties <- map_data("county") %>%
unite(Key, subregion, region, sep = ".", remove = FALSE)
# Join the 2 tibbles
state_join <- left_join(counties, report_03_27_2020, by = c("Key"))
# sum(is.na(state_join$Confirmed))
ggplot(data = us, mapping = aes(x = long, y = lat, group = group)) +
coord_fixed(1.3) +
# Add data layer
borders("state", colour = "black") +
geom_polygon(data = state_join, aes(fill = Confirmed)) +
scale_fill_gradientn(colors = brewer.pal(n = 5, name = "PuRd"),
breaks = c(1, 10, 100, 1000, 10000, 100000),
trans = "log10", na.value = "White") +
ggtitle("Number of Confirmed Cases by US County") +
theme_bw() ## Warning: Transformation introduced infinite values in discrete y-axis
Confirmed Cases in MA
daily_report <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/04-02-2020.csv")) %>%
rename(Long = "Long_") %>%
filter(Province_State == "Massachusetts") %>%
group_by(Admin2) %>%
summarize(Confirmed = sum(Confirmed)) %>%
mutate(Admin2 = tolower(Admin2))## Parsed with column specification:
## cols(
## FIPS = col_double(),
## Admin2 = col_character(),
## Province_State = col_character(),
## Country_Region = col_character(),
## Last_Update = col_character(),
## Lat = col_double(),
## Long_ = col_double(),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Active = col_double(),
## Combined_Key = col_character()
## )us <- map_data("state")
ma_us <- subset(us, region == "massachusetts")
counties <- map_data("county")
ma_county <- subset(counties, region == "massachusetts")
state_join <- left_join(ma_county, daily_report, by = c("subregion" = "Admin2"))
# plot state map
ggplot(data = ma_county, mapping = aes(x = long, y = lat, group = group)) +
coord_fixed(1.3) +
# Add data layer
geom_polygon(data = state_join, aes(fill = Confirmed), color = "white") +
scale_fill_gradientn(colors = brewer.pal(n = 5, name = "BuGn"),
trans = "log10") +
labs(title = "COVID-19 Confirmed Cases in Massachusetts'")
Interactive plotly graph of MA
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutggplotly(
ggplot(data = ma_county, mapping = aes(x = long, y = lat, group = group)) +
coord_fixed(1.3) +
# Add data layer
geom_polygon(data = state_join, aes(fill = Confirmed), color = "black") +
scale_fill_gradientn(colours =
wes_palette("Zissou1", 100, type = "continuous")) +
ggtitle("COVID-19 Cases in MA") +
# Cleaning up the graph
labs(x=NULL, y=NULL) +
theme(panel.border = element_blank()) +
theme(panel.background = element_blank()) +
theme(axis.ticks = element_blank()) +
theme(axis.text = element_blank())
)Exercise 1
daily_report2 <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-26-2020.csv")) %>%
rename(Long = "Long_") ## Parsed with column specification:
## cols(
## FIPS = col_double(),
## Admin2 = col_character(),
## Province_State = col_character(),
## Country_Region = col_character(),
## Last_Update = col_datetime(format = ""),
## Lat = col_double(),
## Long_ = col_double(),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Active = col_double(),
## Combined_Key = col_character(),
## Incidence_Rate = col_double(),
## `Case-Fatality_Ratio` = col_double()
## )ggplot(daily_report2, aes(x = Long, y = Lat, size = Confirmed/1000)) +
borders("world", colour = NA, fill = "grey90") +
theme_bw() +
geom_point(shape = 21, color='purple', fill='purple', alpha = 0.5) +
labs(title = 'World COVID-19 Confirmed cases',x = '', y = '',
size="Cases (x1000))") +
theme(legend.position = "right") +
coord_fixed(ratio=1.5)## Warning: Removed 81 rows containing missing values (geom_point).
#Exercise 2
daily_report2 <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-26-2020.csv")) %>%
rename(Long = "Long_") %>%
filter(Country_Region == "US") %>%
filter (!Province_State %in% c("Alaska","Hawaii", "American Samoa",
"Puerto Rico","Northern Mariana Islands",
"Virgin Islands", "Recovered", "Guam", "Grand Princess",
"District of Columbia", "Diamond Princess")) %>%
filter(Lat > 0)## Parsed with column specification:
## cols(
## FIPS = col_double(),
## Admin2 = col_character(),
## Province_State = col_character(),
## Country_Region = col_character(),
## Last_Update = col_datetime(format = ""),
## Lat = col_double(),
## Long_ = col_double(),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Active = col_double(),
## Combined_Key = col_character(),
## Incidence_Rate = col_double(),
## `Case-Fatality_Ratio` = col_double()
## ) mybreaks <- c(1, 100, 1000, 10000, 100000)
ggplot(daily_report2, aes(x = Long, y = Lat, size = Confirmed)) +
borders("state", colour = "white", fill = "grey90") +
geom_point(aes(x=Long, y=Lat, size=Confirmed, color=Confirmed),stroke=F, alpha=0.7) +
scale_size_continuous(name="Cases", range=c(1,7),
breaks=mybreaks, labels = c("1-99",
"100-999", "1,000-9,999", "10,000-99,999", "50,000+")) +
scale_color_viridis_c(option="viridis",name="Cases",
trans="log", breaks=mybreaks, labels = c("1-99",
"100-999", "1,000-9,999", "10,000-99,999", "50,000+")) +
theme_void() +
guides( colour = guide_legend()) +
labs(title = "Anisa Dhana's lagout for COVID-19 Confirmed Cases in the US on 9/26/2020'") +
theme(
legend.position = "bottom",
text = element_text(color = "#22211d"),
plot.background = element_rect(fill = "#ffffff", color = NA),
panel.background = element_rect(fill = "#ffffff", color = NA),
legend.background = element_rect(fill = "#ffffff", color = NA)
) +
coord_fixed(ratio=1.5)## Warning: Transformation introduced infinite values in discrete y-axis
# Changed the log scale and scale to get the graph to look better #Exercise 3
library(RColorBrewer)
# To display only colorblind-friendly brewer palettes, specify the option colorblindFriendly = TRUE as follow:
# display.brewer.all(colorblindFriendly = TRUE)
# Get and format the covid report data
report_09_26_2020 <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-26-2020.csv")) %>%
rename(Long = "Long_") %>%
unite(Key, Admin2, Province_State, sep = ".") %>%
group_by(Key) %>%
summarize(Confirmed = sum(Confirmed)) %>%
mutate(Key = tolower(Key))## Parsed with column specification:
## cols(
## FIPS = col_double(),
## Admin2 = col_character(),
## Province_State = col_character(),
## Country_Region = col_character(),
## Last_Update = col_datetime(format = ""),
## Lat = col_double(),
## Long_ = col_double(),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Active = col_double(),
## Combined_Key = col_character(),
## Incidence_Rate = col_double(),
## `Case-Fatality_Ratio` = col_double()
## )# dim(report_09_26_2020)
# get and format the map data
us <- map_data("state")
counties <- map_data("county") %>%
unite(Key, subregion, region, sep = ".", remove = FALSE)
# Join the 2 tibbles
state_join <- left_join(counties, report_09_26_2020, by = c("Key"))
# sum(is.na(state_join$Confirmed))
ggplot(data = us, mapping = aes(x = long, y = lat, group = group)) +
coord_fixed(1.3) +
# Add data layer
borders("state", colour = "black") +
geom_polygon(data = state_join, aes(fill = Confirmed)) +
scale_fill_gradientn(colors = brewer.pal(n = 5, name = "Blues"),
breaks = c(1, 10, 100, 1000, 10000, 100000),
trans = "log10", na.value = "White") +
ggtitle("Number of Confirmed Cases by US County") +
theme_dark() ## Warning: Transformation introduced infinite values in discrete y-axis
#Challenge 4
daily_report2 <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-26-2020.csv")) %>%
rename(Long = "Long_") %>%
filter(Province_State == "Florida") %>%
group_by(Admin2) %>%
summarize(Confirmed = sum(Confirmed)) %>%
mutate(Admin2 = tolower(Admin2))## Parsed with column specification:
## cols(
## FIPS = col_double(),
## Admin2 = col_character(),
## Province_State = col_character(),
## Country_Region = col_character(),
## Last_Update = col_datetime(format = ""),
## Lat = col_double(),
## Long_ = col_double(),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Active = col_double(),
## Combined_Key = col_character(),
## Incidence_Rate = col_double(),
## `Case-Fatality_Ratio` = col_double()
## )us <- map_data("state")
fl_us <- subset(us, region == "florida")
counties <- map_data("county")
fl_county <- subset(counties, region == "florida")
state_join <- left_join(fl_county, daily_report2, by = c("subregion" = "Admin2")) library(plotly)
ggplotly(
ggplot(data = fl_county, mapping = aes(x = long, y = lat, group = group)) +
coord_fixed(1.3) +
# Add data layer
geom_polygon(data = state_join, aes(fill = Confirmed), color = "black") +
scale_fill_gradientn(colours =
wes_palette("Darjeeling2", 100, type = "continuous")) +
ggtitle("COVID-19 Cases in FL") +
# Cleaning up the graph
labs(x=NULL, y=NULL) +
theme(panel.border = element_blank()) +
theme(panel.background = element_blank()) +
theme(axis.ticks = element_blank()) +
theme(axis.text = element_blank())
)#Challenge 5
The number of Covid Deaths has only increased as seen by the figures below
## [1] "Aruba" "Anguilla"
## [3] "American Samoa" "Antarctica"
## [5] "French Southern and Antarctic Lands" "Antigua"
## [7] "Barbuda" "Burundi"
## [9] "Saint Barthelemy" "Bermuda"
## [11] "Botswana" "Ivory Coast"
## [13] "Democratic Republic of the Congo" "Republic of Congo"
## [15] "Cook Islands" "Comoros"
## [17] "Cape Verde" "Curacao"
## [19] "Cayman Islands" "Czech Republic"
## [21] "Canary Islands" "Falkland Islands"
## [23] "Reunion" "Mayotte"
## [25] "French Guiana" "Martinique"
## [27] "Guadeloupe" "Faroe Islands"
## [29] "Micronesia" "UK"
## [31] "Guernsey" "Greenland"
## [33] "Guam" "Heard Island"
## [35] "Isle of Man" "Cocos Islands"
## [37] "Christmas Island" "Chagos Archipelago"
## [39] "Jersey" "Siachen Glacier"
## [41] "Kiribati" "Nevis"
## [43] "Saint Kitts" "South Korea"
## [45] "Lesotho" "Saint Martin"
## [47] "Marshall Islands" "Macedonia"
## [49] "Myanmar" "Northern Mariana Islands"
## [51] "Montserrat" "Malawi"
## [53] "New Caledonia" "Norfolk Island"
## [55] "Niue" "Bonaire"
## [57] "Sint Eustatius" "Saba"
## [59] "Nauru" "Pitcairn Islands"
## [61] "Palau" "Puerto Rico"
## [63] "North Korea" "Madeira Islands"
## [65] "Azores" "Palestine"
## [67] "French Polynesia" "Western Sahara"
## [69] "South Sudan" "South Sandwich Islands"
## [71] "South Georgia" "Saint Helena"
## [73] "Ascension Island" "Solomon Islands"
## [75] "Sierra Leone" "Saint Pierre and Miquelon"
## [77] "Sao Tome and Principe" "Swaziland"
## [79] "Sint Maarten" "Turks and Caicos Islands"
## [81] "Tajikistan" "Turkmenistan"
## [83] "Tonga" "Trinidad"
## [85] "Tobago" "Taiwan"
## [87] "USA" "Vatican"
## [89] "Grenadines" "Saint Vincent"
## [91] "Virgin Islands" "Vanuatu"
## [93] "Wallis and Futuna" "Samoa"
## [95] "Yemen"## [1] "Aruba" "Anguilla"
## [3] "American Samoa" "Antarctica"
## [5] "French Southern and Antarctic Lands" "Antigua"
## [7] "Barbuda" "Saint Barthelemy"
## [9] "Bermuda" "Ivory Coast"
## [11] "Democratic Republic of the Congo" "Republic of Congo"
## [13] "Cook Islands" "Cape Verde"
## [15] "Curacao" "Cayman Islands"
## [17] "Czech Republic" "Canary Islands"
## [19] "Falkland Islands" "Reunion"
## [21] "Mayotte" "French Guiana"
## [23] "Martinique" "Guadeloupe"
## [25] "Faroe Islands" "Micronesia"
## [27] "UK" "Guernsey"
## [29] "Greenland" "Guam"
## [31] "Heard Island" "Isle of Man"
## [33] "Cocos Islands" "Christmas Island"
## [35] "Chagos Archipelago" "Jersey"
## [37] "Siachen Glacier" "Kiribati"
## [39] "Nevis" "Saint Kitts"
## [41] "South Korea" "Saint Martin"
## [43] "Marshall Islands" "Macedonia"
## [45] "Myanmar" "Northern Mariana Islands"
## [47] "Montserrat" "New Caledonia"
## [49] "Norfolk Island" "Niue"
## [51] "Bonaire" "Sint Eustatius"
## [53] "Saba" "Nauru"
## [55] "Pitcairn Islands" "Palau"
## [57] "Puerto Rico" "North Korea"
## [59] "Madeira Islands" "Azores"
## [61] "Palestine" "French Polynesia"
## [63] "South Sandwich Islands" "South Georgia"
## [65] "Saint Helena" "Ascension Island"
## [67] "Solomon Islands" "Saint Pierre and Miquelon"
## [69] "Swaziland" "Sint Maarten"
## [71] "Turks and Caicos Islands" "Turkmenistan"
## [73] "Tonga" "Trinidad"
## [75] "Tobago" "Taiwan"
## [77] "USA" "Vatican"
## [79] "Grenadines" "Saint Vincent"
## [81] "Virgin Islands" "Vanuatu"
## [83] "Wallis and Futuna" "Samoa"Although many countries appear to have COVID under control to a degree, the same cannot be said for the US. We will specifically look at the US compared to the former hot spot of Italy
As seen, there is a sharp increase of deaths in the US. now lets look a hot zone such as Texas
As seen, the number of the deaths in high density areas is 100x greater over a 6 month span
References
(R Core Team 2015)
R Core Team. 2015. “R: A Language and Environment for Statistical Computing.” Journal Article. http://www.R-project.org.